The Digital High Definition Television (HDTV) Grand Alliance (Grand Alliance) is a group of television manufacturing and research organizations in the television industry. After years of cooperative effort the Grand Alliance developed and proposed a standard for digital HDTV systems. The Grand Alliance standard has been adopted (with a few changes) by the Federal Communication Commission (FCC) as an official broadcasting standard for HDTV. The standard is known as the Advanced Television Systems Committee Digital Television Standard (the “ATSC Standard”).
The ATSC Standard for HDTV transmission over terrestrial broadcast channels uses a signal that consists of a sequence of twelve (12) independent time-multiplexed trellis-coded data streams modulated as an eight (8) level vestigial sideband (VSB) symbol stream with a rate of 10.76 MHz. This signal is converted to a six (6) MHz frequency band that corresponds to a standard VHF or UHF terrestrial television channel, over which the signal is then broadcast.
The ATSC Standard calls for two (2) bit data symbols of the HDTV signal to be trellis encoded in accordance with an eight (8) level (i.e., a three (3) bit) one dimensional constellation. One bit of each data symbol is pre-coded, and the other is subjected to a ½ encoding rate that produces two coded bits in accordance with a four (4) state trellis code. For purposes of interleaving, twelve (12) identical encoders and pre-coders operate successively on every twelve successive data symbols. Symbols 0, 12, 24, 36, . . . are encoded as one series. Symbols 1, 13, 25, 37, . . . as a second series. Symbols 2, 14, 26, 38, . . . as a third series. And so on for a total of twelve (12) series. Therefore, the ATSC Standard requires twelve (12) trellis decoders in the HDTV receiver for the twelve (12) series of time division interleaved data symbols in the signal. Each trellis decoder in the HDTV receiver decodes every twelfth (12th) data symbol in the stream of coded data symbols.
In an ATSC Standard receiver trellis decoders are used to retrieve the original digital data that was trellis encoded just before being converted to 8-VSB symbols, modulated and broadcast. The use of trellis coding provides an improvement in the signal to noise ratio of the received signal, and the time multiplexing of twelve (12) independent streams reduces the possibility of co-channel interference from an analog NTSC broadcast signal residing on the same frequency. The abbreviation NTSC stands for National Television Standards Committee.
Each of the trellis decoders for the four (4) state trellis code operates in accordance with the well-known Viterbi decoding algorithm. Each of the decoders comprises a branch metric generator unit, an add-compare-select unit, and a path-memory unit. See, for example, “Trellis-coded Modulation With Redundant Signal Set, Part I, Introduction; Part II, State of the Art,” by G. Ungerboeck, IEEE Communications Magazine, Vol. 25, pp. 5–21, February 1987.
In addition to being corrupted by noise, the transmitted signal is also subject to deterministic channel distortions and distortions caused by multipath interference. Consequently, an adaptive channel equalizer is generally used in front of the trellis decoders to compensate for these effects. The goal is to create a symbol stream that resembles, as much as possible, the symbol stream that was created by the twelve (12) trellis encoders at the transmitter.
One commonly used equalizer architecture makes use of a second equalizer known as a decision feedback equalizer (DFE). In this architecture, a conventional, or forward equalizer (FE) is supplemented by a DFE. The input to the DFE is an estimate of the original transmitted value of the current output symbol of the complete equalizer (FE and DFE). The output of the decision feedback equalizer (DFE) is subsequently added to the output of the forward equalizer (FE) to generate the output symbol. In a typical implementation, this estimate of the output symbol is obtained by simply “slicing” the equalizer output. The term “slicing” refers to the process of taking the allowed symbol value (of the eight (8) levels specified by the 8-VSB ATSC Standard) that is nearest to that of the actual output. Using the “sliced” symbols in a decision feedback equalizer (DFE) gives a near optimum error rate performance with low complexity. This approach, however, can suffer from error propagation caused by slicing errors. Because the typical symbol error rate after the equalizer for the HDTV signal can be up to twenty percent (20%), this can be a serious problem if the number of DFE filter taps is large.
After the equalizer, the HDTV signal is decoded in a trellis decoder that uses the Viterbi algorithm to decode the symbol stream based on the ½ rate trellis coding performed in the transmitter. As previously mentioned, the ATSC Standard specifies that twelve (12) trellis encoders and decoders are used in parallel in a time multiplexed fashion. Trellis decoding is then followed by byte de-interleaving and Reed Solomon decoding to further correct transmission errors in the signal.
Various prior art algorithms exist for computing the filter coefficients for adaptive equalizers. One commonly used method uses the well known least mean square (LMS) algorithm. The LMS algorithm is a successive approximation technique that uses the current coefficient and data tap values as well as the equalizer error to compute the new coefficient value. The LMS algorithm repeats the procedure until each filter coefficient converges to the desired optimum value.
In a typical LMS algorithm the coefficient vector is determined using the following formula:Cn+1=Cn+μE dn  (2)where Cn is the coefficient vector at time n, μ is an adaptation speed constant, and dn is the data vector in the filter at time n. E is the error calculated from the output of the equalizer. E can be calculated in a decision directed fashion using the known training sequence embedded in the data stream. Alternatively, E can be calculated in a blind fashion using a Constant Modulus Algorithm (CMA) or a Decision Directed (DD) algorithm.
Transmission channels are often sparse. Optimally designed adaptive equalizers reflect the sparsity in the transmission channels. Typical prior art equalization methods such as the Least Mean Square (LMS) algorithm or the Constant Modulus Algorithm (CMA) or the Decision Directed (DD) algorithm do not exploit known “a priori” information for sparse transmission channels. Typical prior art approaches to exploiting sparsity attempt to reduce complexity. Complexity reduction is accomplished by only updating a subset of the channel model or equalizer taps.
Recently a prior art “exponentiated gradient” (EG) algorithm has been shown to have better performance than typical gradient methods when the target weight vector is sparse. See the article entitled “Exponentiated Gradient Versus Gradient Descent for Linear Predictors” by J. Kivinen and M. K. Warmuth in Information and Computation, 132(1), pp. 1–64, 1997. Unfortunately, the EG algorithm has several limitations that make it inappropriate for applications in communications and signal processing. For example, the EG algorithm does not decrease the required complexity.
There is therefore a need in the art for an apparatus and method for providing adaptive equalization algorithms capable of exploiting sparsity in target weight vectors in adaptive channel equalizers in general and in ATSC VSB receivers in particular.